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An Open and Data-driven Taxonomy of Skills Extracted from Online Job Adverts

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  • Jyldyz Djumalieva1
  • Cath Sleeman

Abstract

In this work we offer an open and data-driven skills taxonomy, which is independent of ESCO and O*NET, two popular available taxonomies that are expert-derived. Since the taxonomy is created in an algorithmic way without expert elicitation, it can be quickly updated to reflect changes in labour demand and provide timely insights to support labour market decision-making. Our proposed taxonomy also captures links between skills, aggregated job titles, and the salaries mentioned in the millions of UK job adverts used in this analysis. To generate the taxonomy, we employ machine learning methods, such as word embeddings, network community detection algorithms and consensus clustering. We model skills as a graph with individual skills as vertices and their co-occurrences in job adverts as edges. The strength of the relationships between the skills is measured using both the frequency of actual co-occurrences of skills in the same advert as well as their shared context, based on a trained word embeddings model. Once skills are represented as a network, we hierarchically group them into clusters. To ensure the stability of the resulting clusters, we introduce bootstrapping and consensus clustering stages into the methodology. While we share initial results and describe the skill clusters, the main purpose of this paper is to outline the methodology for building the taxonomy.

Suggested Citation

  • Jyldyz Djumalieva1 & Cath Sleeman, 2018. "An Open and Data-driven Taxonomy of Skills Extracted from Online Job Adverts," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-13, Economic Statistics Centre of Excellence (ESCoE).
  • Handle: RePEc:nsr:escoed:escoe-dp-2018-13
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    References listed on IDEAS

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    1. Martin Rosvall & Carl T Bergstrom, 2010. "Mapping Change in Large Networks," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-7, January.
    2. Jyldyz Djumalieva & Antonio Lima & Cath Sleeman, 2018. "Classifying Occupations According to Their Skill Requirements in Job Advertisements," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-04, Economic Statistics Centre of Excellence (ESCoE).
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    Cited by:

    1. Stef Garasto & Jyldyz Djumalieva & Karlis Kanders & Rachel Wilcock & Cath Sleeman, 2021. "Developing experimental estimates of regional skill demand," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-02, Economic Statistics Centre of Excellence (ESCoE).
    2. Seifried, Mareike & Jurowetzki, Roman & Kretschmer, Tobias, 2020. "Career paths in online labor markets: Same, same but different?," ZEW Discussion Papers 20-090, ZEW - Leibniz Centre for European Economic Research.
    3. Seifried, Mareike, 2021. "Transitions from offline to online labor markets: The relationship between freelancers' prior offline and online work experience," ZEW Discussion Papers 21-101, ZEW - Leibniz Centre for European Economic Research.
    4. Leonardo Fabio Morales & Carlos Ospino & Nicole Amaral, 2021. "Online Vacancies and its Role in Labor Market Performance," Borradores de Economia 1174, Banco de la Republica de Colombia.
    5. Jyldyz Djumalieva & Stef Garasto & Cath Sleeman, 2020. "Evaluating a new earnings indicator. Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts?," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-19, Economic Statistics Centre of Excellence (ESCoE).
    6. Jagjit S. Chadha & Richard Barwell, 2019. "Renewing our Monetary Vows: Open Letters to the Governor of the Bank of England," National Institute of Economic and Social Research (NIESR) Occasional Papers 58, National Institute of Economic and Social Research.

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    More about this item

    Keywords

    Skills; Skills taxonomy; Labour demand; Online job adverts; Big data; Machine learning; Word embeddings;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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